Search

In the dry zone of Sri Lanka experiencing hydrological and weather extremes, non-availability of validated scientific data on stream-flows and groundwater stocks for sufficient time periods, and the presence of large numbers of small and large tanks that divert the water from the streams, make water resource planning a very complex challenge. An integrated modelling exercise was carried out: first using a hydrological simulation model to assess the annual and seasonal water flows of Yan Oya basin of Anuradhapura district and its seven sub-watersheds for 30 years and their dependability; then using a water balance model to analyze the extent to which the current water demands of five selected cascades are being met from the supplies available; and assessing the scope for augmenting the existing storage capacity of the tanks to reduce the irrigation deficits. This article presents the results of the study with regard to: the basin flows and their variability; and water balance scenarios for five selected cascades, with and without modifications in tank storage capacity. The article also discusses how the results from such integrated modelling studies should be used by irrigation planners for taking water management decisions for the basin with decentralized storages like tanks. HIGHLIGHTS Water resources planning around small systems is complex.; Non-availability of scientific data on stream-flows, and the presence of large number of small water systems are major challenges.; SWAT modelling was done to assess the yield of Yan Oya basin.; The rainfall-runoff model estimated for the basin showed linear relationship.; Analysis of five cascades with 30 tanks showed that in 16 cases, supply deficit existed.

Groundwater vulnerability assessment has become a crucial step in successfully protecting groundwater against pollution. An attempt of this study has been made to evaluate groundwater contamination risk using intrinsic vulnerability and land-uses in Vanathavillu, Kalpitiya and Katana area in Sri Lanka, using coupled DRASTIC with GIS as feasible methodology. The findings reveal that the groundwater in the areas under study falls under very low to high contamination risk. The higher risk of contamination has been identified in most of the Kalpitiya (about 82%) with the moderate along the beach in the west and next to Puttalam lagoon in the northeast and southeast. This is mainly due to pollution risk inherent with intense vegetable cultivation, over pumping, shallow groundwater tables and permeable sandy soil. Vanathavillu is under very low to moderate contamination risk, in which the moderate risk (about 13%) has especially been found the center, central southwest and west of the area. The relative less deep groundwater tables, possible seepage from the underlying limestone aquifer and less permeable red earth soil could be cause for the moderate risk in the area. Furthermore, results show that the Katana has low to moderately high groundwater contamination risk. Nitrate has a good agreement with the different pollution risk classes and that nitrate can be used as an indicator of aquifer degradation inherent with land-use activities in the coastal areas. Groundwater quality monitoring network should be set up to minimize the anthropogenic acts, particularly in high and moderate contamination risk zones.

An efficient compact encoding process with the pipelining design of Low Density Parity Check (LDPC) Encoder with a two stage, three stage and Maximal Rate Pipelining (MRP) structures are proposed in this work. Comparatively, LDPC encoding is more complex than decoding. The complexity mainly referred to the mathematical computation involved in the design of LDPC encoder. There are several methods used to design reduced parity check matrix, one of the methods involved is the Gauss Elimination method. Conventional and wave pipelining techniques are implemented to overcome the disadvantages of the area, latency, etc., The designed architectures are synthesized in SYNOPSYS tool and implemented in the XC3S-250E FPGA device. The overheads like power and area are efficiently reduced by wave pipelining and their efficiency can be proved by synthesis report. This showcases that the LDPC Encoders require less memory by using MRP structure.

The design is focused on fabrication and testing of frequency reconfigurable antenna designed to operate in LTE and Wi-Fi implementations. The reconfigurable mechanism is governed by three PIN diodes and the operating bandwidth (1.38 GHz - 3.24 GHz) is efficiently controlled by diode’s different operating states. The antenna’s working principle is elucidated with the reflection coefficient, VSWR, gain and directivity characteristics. The antenna exhibits a maximum gain of 2.23 dBi and the VSWR of less than 1.5 throughout the bandwidth. The ON and OFF conditions of the diode go well with the desired operation and it does not induce the cross polarization. This work aims to design the simulations which are conceded using ANSYS HFSS full wave EM simulator and the measurements are in accordance with the simulated one.

Wireless sensor networks (WSNs) have a significant number of sensing nodes with computing and communication resources for data transmission. Many essential aspects such as computational power, storage capability, and energy consumption must be taken into account to use WSNs for data transmission. Previous research work developed a cross layer security based intrusion detection system (CLS-IDS) framework that has improved network performance, minimal energy consumption and to prolong network life, meeting QoS requirements. However, this CLS-IDS method cannot detect the faults happening at various places at the same time. So, this research work proposes the cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) and also least overhead monitoring for WSNs by means of memory and energy demands to resolve the above issue. This cross-layer security was designed with the aid of a fuzzy logic-based calculation method for a protocol called cross-layer protocol, uses multiple parameters extracted by the exchange of inter-layer information in order to alleviate the effects of security hazards to the network of wireless sensors. In order to identify malicious nodes on the network, the fault monitoring system is carried out using enhanced convolutional neural network (ECNN) classifier and the confidential value is determined for the trust values. The proposed cross layer security based fuzzy trust mechanism (CLS-FLTCM) is highly efficient technique to ensure optimum safety in wireless sensor environment when compared to the previous techniques. The simulation results conducted using the NS-2 simulator and testing the safety performance of the proposed system demonstrates that this approach is better than the current detection precision techniques.

Zinc oxide (ZnO) hybridized with graphitic carbon nitride (g-C3N4) composite was prepared via one step calcination method and well characterized using various physiochemical techniques. The prepared composite exhibits excellent photocatalytic activity and stability for decolorization of methylene blue (MB) dye solution under visible light irradiation. Effect of various rate determining parameters such as catalyst loading, initial dye concentration and pH on the decolorization of MB has been analyzed. The optimum conditions for efficient color removal were found to be 7, 10 ppm and 2 g/L for pH, dye concentration and catalyst dosage respectively. The intermediate compounds formed during the decolorization process were evaluated by GCMS spectra. It was inferred that the ZnO/g-C3N4 (98.83%) composite exhibits highest decolorization efficiency as compare with pure g-C3N4 (35.21%). Such enhancement of photocataytic activity is mainly attributed to the efficient separation of photo induced electron hole pairs via Z-scheme model composed of ZnO and g-C3N4.

Despite the ability of Artificial Neural Network (ANN) to handle nonlinear relationships in data, there are instances where ANNs have not been able to predict accurately in the presence of extremes values or other inherent groupings in the data. Although the ANN modeling expects the data to be evenly distributed over an entire data space, in practical situations data often consist of clusters or extreme values. Thus, instead of modeling the data as it is, appropriate mechanisms should be followed to handle those inconsistancies. This paper presents one such mechanism based on two clustering algorithms, k-means and fuzzy c-means. The base model is Nonlinear Autoregressive Artificial Neural Network (NAR-ANN). Altogether 14 model formulations of NAR-ANN were compared here with varying number of clusters and a trimming mechanism. Results suggest the superiority of cluster based NAR-ANN over the single NAR-ANN. Modular ANN approach with an optimum combination of the 14 models can be used for better results. The proposed cluster based NAR-ANN used in this paper is a novel generalization to NAR-ANN where the cluster effect is incorporated as a binary exogenous variable (NARX).

The aim of this study was to analyse the annual and seasonal rainfall trends in Sri Lanka during the 50 year period from 1966 to 2015 using rainfall data collected at 32 rain gauging stations. The seasons considered were the two principal monsoon seasons and the two inter-monsoon seasons. Non-parametric Mann-Kendall trend test and Sen’s Slope estimator method were used to examine the rainfall trends and determine their magnitudes. According to the analysis which was based on annual rainfall data, 21 of the gauging stations were showing increasing trends. The balance 11 stations were showing decreasing trends. Four of the stations, namely those at Anuradhapura, Batticaloa, Mapakadawewa and Pottuvil had significantly increasing trends. The three stations at Chilaw, Dandeniya Tank and Iranamadu Tank showed significantly decreasing trends. In general, the eastern region of the country has shown over the last half century an increasing rainfall trend and the western, northern and south western regions and the central hills of the country have shown a decreasing rainfall trend during the same period. The seasonal rainfall indicated increasing trends during the First-Inter Monsoon, Second-Inter Monsoon and Northwest Monsoon seasons at a majority of the stations. In contrast, during the Southwest Monsoon season, most of the gauging stations have shown downward rainfall trends.

The availability of a long and complete rainfall record is very important for carrying out a hydrological study successfully. However in general, the data series in these records may contain gaps for various reasons. The objective of this study is to analyse the different methods available for filling gaps in rainfall data records and propose a method suitable for a river basin situated in a mountainous area in Sri Lanka. Towards this end, daily rainfall data from ten gauging stations in the upper catchment area of BaduluOya were collected. Seven different techniques were studied to ascertain their suitability. The methods studied were the Arithmetic Mean method, Normal Ratio method, Inverse Distance Weighting method, Linear Regression method, Weighted Linear Regression method, Multiple Linear Regression method and the Probabilistic method. The data generated for the target stations were compared with actual observations made, based on error statistics, Error Standard Deviation (STD),Root Mean Square Error (RMSE) and Correlation Coefficient (CC). The results of the study showed that for target stations that have only one neighbouring station with a high correlation coefficient, the Probabilistic method and the Linear Regression method give good predictions. For stations that have relatively low correlation coefficients with the neighbouring stations, the Inverse Distance Squared method and the Normal Ratio method outperformed the others. To obtain accurate results from the Multiple Linear Regression method and the Weighted Linear Regression method, it is necessary to have a set of neighbouring stations that have fairly high correlation coefficients with the target station.

Lumped rainfall-runoff models are popular among hydrologists as they are convenient for modeling basins. The accuracy of a lumped model could be increased by dividing the catchment into several sub-catchments to represent variations in the basin. This paper discusses the improvements that could be achieved by considering a catchment in several sub-catchments. In this study, lumped model HEC-HMS has been used to model Kalu Ganga River catchment upto Ratnapura. The paper compares the diverse modeling methods available in the HEC-HMS software for modeling losses, modeling transformation of rainfall to runoff and modeling baseflow. The results indicate that the performance of the rainfall-runoff model significantly improves when the basin is modeled in many sub-catchments. Further, the study shows that alternative methods available for loss, transform and baseflow in the HEC-HMS perform differently. For Kalu Ganga River basin the deficit constant method, Snyder unit hydrograph method and recession method were found to be the most suitable methods for the three components.